• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12C
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• pp.190-198
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• 2001

In this paper, the authors propose methods for determining the differentiated price for elastic traffic in IP (Internet Protocol) network. First, we investigate the behavior in the consumption of bandwidth of elastic traffic in IP network. Next, we propose a method to relate the bandwidth usage with the pricing for the elastic traffic, which is based partially or fully on the usage rate of the network bandwidth. After that, we propose a charging function for elastic traffic, which is based on the de facto usage of the bandwidth. Finally, we will illustrate the implication of the work via simple numerical experiments.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.247-252
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• 2014

Traffic network is composed of passing vehicls, delayed vehicles, traffic situation which is traffic incomes of traffic interfacing system. Traffic green time light is concluded by inside input factor, that is green light cycle, yellow light cycle, led light cycle, which light cycle is sensor inputs. That light cycle is converted to traffic phase composed of passing peoples and delayed vehicles, whose intervals is concluding of traffic network factors composed of consumptiom power factors, delayed time situation, occupying sensor nodes. This is very important sector,because of much poor traffic situation.

• Convergence Security Journal
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• v.2 no.2
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• pp.9-17
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• 2002

Traffic engineering function consists of traffic management, capacity management and network planning. In this paper, we present the requirements for each functional traffic management, and also present functional and process model to efficiently to handle the traffic engineering for multimedia internet services. Finally, the traffic management methods for each step are described in detail.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2B
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• pp.217-227
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• 2004

With a rapid growth in the Internet technology, the network traffic is increasing swiftly. As for the increase of traffic, it had a large influence on performance of a total network. Therefore, a traffic management became an important issue of network management. In this paper, we study a forecast plan of network traffic in order to analyze network traffic and to establish efficient correspondence. We use time series forecast models and determine fitness whether the model can forecast network traffic exactly. In order to predict a model, AR, MA, ARMA, and ARIMA must be applied. The suitable model can be found that can express the nature of traffic for the forecast among these models. We determines whether it is satisfied with stationary in the assumption step of the model. The stationary can get the results by using ACF(Auto Correlation Function) and PACF(Partial Auto Correlation Function). If the result of this function cannot satisfy then the forecast model is unsuitable. Therefore, we are going to get the correct model that is to satisfy stationary assumption. So, we proposes a way to classify in order to get time series materials to satisfy stationary. The correct prediction method is managed traffic of a network with a way to be better than now. It is possible to manage traffic dynamically if it can be used.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.336-342
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• 2017

Traffic can be localized by reducing the traffic load on the physical network by causing traffic to be generated at the end of the packet network. By localizing traffic, the IoT-based sensitive data-related security issues can be supported effectively. In addition, it can be applied effectively to the next-generation smart network environment without changing the existing network infrastructure. In this paper, a content priority scheme was applied to smart network-based IoT data. The IoT contents were localized to efficiently pinpoint the flow of traffic on the network to enable smart forwarding. In addition, research was conducted to determine the effective network traffic routes through content localization. Through this study, the network load was reduced. In addition, it is a network structure that can guarantee user quality. In addition, it proved that the IoT service can be accommodated effectively in a smart network-based environment.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.3
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• pp.122-131
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• 2009

Ubiquitous-Transportation sensor network is able to realize a vehicle ad-hoc network. Since there are some problems in an existing ITS system, the new technology and traffic information strategies are requirements in this advanced system, u-TSN. The purposes of this paper is to introduce the components on u-TSN system, establish new traffic strategies for this system, and then evaluate these strategies by making a comparative study of ITS and using micro traffic simulator, AIMSUN. The strategy evaluated by AIMSUN is position-based multicast strategy which provides traffic information to vehicles using V2I (vehicle to Infrastructure) communication. This paper focuses on the providing real-time route guidance information when congestion is occurred by the incidents. This study estimates total travel time on each route by API modules. Result from simulation experiments suggests that position-based multicast strategy can achieve more optimal network performance and increased driver satisfaction since the total accumulated travel times of both the major road and the total system on position-based multicast strategy are less than those on VMS.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.209-214
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• 2016

IEC 61850 is a protocol used to reduce the cost of design, installation and maintenance of the Substation Automation System. GOOSE traffic used in IEC 61850 plays an important role for control, protection and automation of the substation. This study implemented a GOOSE traffic generator using the emulation function of NS-3 network simulator, by using protocols provided by a network simulator and another protocols provided by real communication equipment. The generated GOOSE traffic was analyzed with Wireshark, and it was found that the traffic was generated exactly as expected. Besides, this study measured the GOOSE traffic delay due to the increase of the number of switches according to network topology. It is expected that the GOOSE traffic generator implemented by this study will be efficiently used when experiments are performed on actual substation environments.

• Korean Management Science Review
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• v.23 no.2
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• pp.161-174
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• 2006

In this paper, we consider a network optimization problem arising from the deployment of BcN access network. BcN convergence services requires that access networks satisfy QoS meausres. BcN services have two types of traffics : stream traffic and elastic traffic. Stream traffic uses blocking probability as a QoS measure, while elastic traffic uses delay factor as a QoS measure. Incorporating the QoS requirements, we formulate the problem as a nonlinear mixed-integer Programming model. The Proposed model seeks to find a minimum cost dimensioning solution, while satisfying the QoS requirement. We propose two local search heuristic algorithms for solving the problem, and develop a network design system that implements the developed heuristic algorithms. We demonstrate the computational efficacy of the proposed algorithm by solving a realistic network design problem.

• Journal of the Korea Society for Simulation
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• v.8 no.2
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• pp.45-56
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• 1999

It is necessary that we should control the traffic to not only efficiently use the rich bandwidth of ATM network but also satisfy the users various requirements for service quality. However, it is very difficult to decide which control mechanism would be applied in real network because there are various types of ATM traffic and traffic control mechanisms. In this paper, a smart simulator is developed ot analyze the performance of a UPC(Usage Parameter Control) mechanism which is a typical traffic control mechanism. The simulator consists of a user interface that supports a menu-driven input form and a simulation program that is executed with the users input parameters. Especially, the simulator establishes more powerful and flexible simulation environment since it supports a more complex simulation applying various source traffic to several different UPC mechanisms at the same time and allows an arbitrary user-defined traffic in addition to some well-known traffic.